# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
 * Adapted from BLIP (https://github.com/salesforce/BLIP)
 * Based on huggingface code base
 * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
"""
import math
from typing import Optional, Tuple

import paddle
import paddlenlp
from paddlenlp.transformers import BertConfig
from paddlenlp.transformers.activations import ACT2FN
from paddlenlp.transformers.model_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    BaseModelOutputWithPoolingAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
)
from paddlenlp.transformers.model_utils import (
    apply_chunking_to_forward,
    find_pruneable_heads_and_indices,
    prune_linear_layer,
)

logger = paddle.utils.try_import("logging").getLogger(name=__name__)


class BertEmbeddings(paddle.nn.Layer):
    """Construct the embeddings from word and position embeddings."""

    def __init__(self, config):
        super().__init__()
        self.word_embeddings = paddle.nn.Embedding(
            num_embeddings=config.vocab_size,
            embedding_dim=config.hidden_size,
            padding_idx=config.pad_token_id,
        )
        self.position_embeddings = paddle.nn.Embedding(
            num_embeddings=config.max_position_embeddings,
            embedding_dim=config.hidden_size,
        )
        self.LayerNorm = paddle.nn.LayerNorm(normalized_shape=config.hidden_size, epsilon=config.layer_norm_eps)
        self.dropout = paddle.nn.Dropout(p=config.hidden_dropout_prob)
        self.register_buffer(
            name="position_ids",
            tensor=paddle.arange(end=config.max_position_embeddings).expand(shape=(1, -1)),
        )
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        self.config = config

    def forward(
        self,
        input_ids=None,
        position_ids=None,
        inputs_embeds=None,
        past_key_values_length=0,
    ):
        if input_ids is not None:
            input_shape = tuple(input_ids.shape)
        else:
            input_shape = tuple(inputs_embeds.shape)[:-1]
        seq_length = input_shape[1]
        if position_ids is None:
            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)
        embeddings = inputs_embeds
        if self.position_embedding_type == "absolute":
            position_embeddings = self.position_embeddings(position_ids)
            embeddings += position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class BertSelfAttention(paddle.nn.Layer):
    def __init__(self, config, is_cross_attention):
        super().__init__()
        self.config = config
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention heads (%d)"
                % (config.hidden_size, config.num_attention_heads)
            )
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query = paddle.nn.Linear(in_features=config.hidden_size, out_features=self.all_head_size)
        if is_cross_attention:
            self.key = paddle.nn.Linear(in_features=config.encoder_width, out_features=self.all_head_size)
            self.value = paddle.nn.Linear(in_features=config.encoder_width, out_features=self.all_head_size)
        else:
            self.key = paddle.nn.Linear(in_features=config.hidden_size, out_features=self.all_head_size)
            self.value = paddle.nn.Linear(in_features=config.hidden_size, out_features=self.all_head_size)
        self.dropout = paddle.nn.Dropout(p=config.attention_probs_dropout_prob)
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = paddle.nn.Embedding(
                num_embeddings=2 * config.max_position_embeddings - 1,
                embedding_dim=self.attention_head_size,
            )
        self.save_attention = False

    def save_attn_gradients(self, attn_gradients):
        self.attn_gradients = attn_gradients

    def get_attn_gradients(self):
        return self.attn_gradients

    def save_attention_map(self, attention_map):
        self.attention_map = attention_map

    def get_attention_map(self):
        return self.attention_map

    def transpose_for_scores(self, x):
        new_x_shape = tuple(x.shape)[:-1] + (
            self.num_attention_heads,
            self.attention_head_size,
        )
        x = x.reshape(new_x_shape)
        return x.transpose(perm=[0, 2, 1, 3])

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):
        mixed_query_layer = self.query(hidden_states)
        is_cross_attention = encoder_hidden_states is not None
        if is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
            key_layer = paddle.concat(x=[past_key_value[0], key_layer], axis=2)
            value_layer = paddle.concat(x=[past_key_value[1], value_layer], axis=2)
        else:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)
        past_key_value = key_layer, value_layer
        attention_scores = paddle.matmul(x=query_layer, y=key_layer.transpose((0, 1, 3, 2)))
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            seq_length = tuple(hidden_states.shape)[1]
            position_ids_l = paddle.arange(dtype="int64", end=seq_length).reshape([-1, 1])
            position_ids_r = paddle.arange(dtype="int64", end=seq_length).reshape([1, -1])
            distance = position_ids_l - position_ids_r
            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)
            if self.position_embedding_type == "relative_key":
                relative_position_scores = paddle.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = paddle.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                relative_position_scores_key = paddle.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            attention_scores = attention_scores + attention_mask
        attention_probs = paddle.nn.Softmax(axis=-1)(attention_scores)
        if is_cross_attention and self.save_attention:
            self.save_attention_map(attention_probs)
            attention_probs.register_hook(hook=self.save_attn_gradients)
        attention_probs_dropped = self.dropout(attention_probs)
        if head_mask is not None:
            attention_probs_dropped = attention_probs_dropped * head_mask
        context_layer = paddle.matmul(x=attention_probs_dropped, y=value_layer)
        context_layer = context_layer.transpose(perm=[0, 2, 1, 3]).contiguous()
        new_context_layer_shape = tuple(context_layer.shape)[:-2] + (self.all_head_size,)

        context_layer = context_layer.reshape(new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        outputs = outputs + (past_key_value,)
        return outputs


class BertSelfOutput(paddle.nn.Layer):
    def __init__(self, config):
        super().__init__()
        self.dense = paddle.nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size)
        self.LayerNorm = paddle.nn.LayerNorm(normalized_shape=config.hidden_size, epsilon=config.layer_norm_eps)
        self.dropout = paddle.nn.Dropout(p=config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BertAttention(paddle.nn.Layer):
    def __init__(self, config, is_cross_attention=False):
        super().__init__()
        self.self = BertSelfAttention(config, is_cross_attention)
        self.output = BertSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads,
            self.self.num_attention_heads,
            self.self.attention_head_size,
            self.pruned_heads,
        )
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):
        self_outputs = self.self(
            hidden_states,
            attention_mask,
            head_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            past_key_value,
            output_attentions,
        )
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs


class BertIntermediate(paddle.nn.Layer):
    def __init__(self, config):
        super().__init__()
        self.dense = paddle.nn.Linear(in_features=config.hidden_size, out_features=config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


class BertOutput(paddle.nn.Layer):
    def __init__(self, config):
        super().__init__()
        self.dense = paddle.nn.Linear(in_features=config.intermediate_size, out_features=config.hidden_size)
        self.LayerNorm = paddle.nn.LayerNorm(normalized_shape=config.hidden_size, epsilon=config.layer_norm_eps)
        self.dropout = paddle.nn.Dropout(p=config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BertLayer(paddle.nn.Layer):
    def __init__(self, config, layer_num):
        super().__init__()
        self.config = config
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = BertAttention(config)
        self.layer_num = layer_num
        if self.config.add_cross_attention:
            self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention)
        self.intermediate = BertIntermediate(config)
        self.output = BertOutput(config)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
        mode=None,
    ):
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        self_attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            head_mask,
            output_attentions=output_attentions,
            past_key_value=self_attn_past_key_value,
        )
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:-1]
        present_key_value = self_attention_outputs[-1]
        if mode == "multimodal":
            assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
            cross_attention_outputs = self.crossattention(
                attention_output,
                attention_mask,
                head_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                output_attentions=output_attentions,
            )
            attention_output = cross_attention_outputs[0]
            outputs = outputs + cross_attention_outputs[1:-1]
        layer_output = apply_chunking_to_forward(
            self.feed_forward_chunk,
            self.chunk_size_feed_forward,
            self.seq_len_dim,
            attention_output,
        )
        outputs = (layer_output,) + outputs
        outputs = outputs + (present_key_value,)
        return outputs

    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output


class BertEncoder(paddle.nn.Layer):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = paddle.nn.LayerList(sublayers=[BertLayer(config, i) for i in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
        mode="multimodal",
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
        next_decoder_cache = () if use_cache else None
        for i in range(self.config.num_hidden_layers):
            layer_module = self.layer[i]
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)
            layer_head_mask = head_mask[i] if head_mask is not None else None
            past_key_value = past_key_values[i] if past_key_values is not None else None
            if self.gradient_checkpointing and self.training:
                if use_cache:
                    logger.warn(
                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                    )
                    use_cache = False

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs, past_key_value, output_attentions)

                    return custom_forward

                layer_outputs = paddle.distributed.fleet.utils.recompute(
                    create_custom_forward(layer_module),
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    mode=mode,
                )
            else:
                layer_outputs = layer_module(
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    past_key_value,
                    output_attentions,
                    mode=mode,
                )
            hidden_states = layer_outputs[0]
            if use_cache:
                next_decoder_cache += (layer_outputs[-1],)
            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    next_decoder_cache,
                    all_hidden_states,
                    all_self_attentions,
                    all_cross_attentions,
                ]
                if v is not None
            )
        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_decoder_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            cross_attentions=all_cross_attentions,
        )


class BertPooler(paddle.nn.Layer):
    def __init__(self, config):
        super().__init__()
        self.dense = paddle.nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size)
        self.activation = paddle.nn.Tanh()

    def forward(self, hidden_states):
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


class BertPredictionHeadTransform(paddle.nn.Layer):
    def __init__(self, config):
        super().__init__()
        self.dense = paddle.nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size)
        if isinstance(config.hidden_act, str):
            self.transform_act_fn = ACT2FN[config.hidden_act]
        else:
            self.transform_act_fn = config.hidden_act
        self.LayerNorm = paddle.nn.LayerNorm(normalized_shape=config.hidden_size, epsilon=config.layer_norm_eps)

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


class BertLMPredictionHead(paddle.nn.Layer):
    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)
        self.decoder = paddle.nn.Linear(
            in_features=config.hidden_size,
            out_features=config.vocab_size,
            bias_attr=False,
        )
        self.bias = paddle.base.framework.EagerParamBase.from_tensor(tensor=paddle.zeros(shape=config.vocab_size))
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states


class BertOnlyMLMHead(paddle.nn.Layer):
    def __init__(self, config):
        super().__init__()
        self.predictions = BertLMPredictionHead(config)

    def forward(self, sequence_output):
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores


class BertPreTrainedModel(paddlenlp.transformers.PretrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = BertConfig
    base_model_prefix = "bert"
    _keys_to_ignore_on_load_missing = ["position_ids"]


class BertModel(BertPreTrainedModel):
    """
    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
    input to the forward pass.
    """

    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config
        self.embeddings = BertEmbeddings(config)
        self.encoder = BertEncoder(config)
        self.pooler = BertPooler(config) if add_pooling_layer else None
        self.init_weights()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def invert_attention_mask(self, encoder_attention_mask: paddle.Tensor) -> paddle.Tensor:
        """
        Invert an attention mask (e.g., switches 0. and 1.).
        Args:
            encoder_attention_mask (`paddle.Tensor`): An attention mask.
        Returns:
            `paddle.Tensor`: The inverted attention mask.
        """
        if encoder_attention_mask.ndim == 3:
            encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
        if encoder_attention_mask.ndim == 2:
            encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
        # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
        # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
        # /transformer/transformer_layers.py#L270
        # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
        # encoder_extended_attention_mask.transpose(-1, -2))
        encoder_extended_attention_mask = encoder_extended_attention_mask.cast(
            dtype=self.config.dtype
        )  # fp16 compatibility
        encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e4

        return encoder_extended_attention_mask

    def get_head_mask(
        self, head_mask: Optional[paddle.Tensor], num_hidden_layers: int, is_attention_chunked: bool = False
    ) -> paddle.Tensor:
        """
        Prepare the head mask if needed.
        Args:
            head_mask (`paddle.Tensor` with shape `[num_heads]` or `[num_hidden_layers x num_heads]`, *optional*):
                The mask indicating if we should keep the heads or not (1.0 for keep, 0.0 for discard).
            num_hidden_layers (`int`):
                The number of hidden layers in the model.
            is_attention_chunked: (`bool`, *optional*, defaults to `False`):
                Whether or not the attentions scores are computed by chunks or not.
        Returns:
            `paddle.Tensor` with shape `[num_hidden_layers x batch x num_heads x seq_length x seq_length]` or list with
            `[None]` for each layer.
        """
        if head_mask is not None:
            head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
            if is_attention_chunked is True:
                head_mask = head_mask.unsqueeze(-1)
        else:
            head_mask = [None] * num_hidden_layers

        return head_mask

    def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers):
        """-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]"""
        if head_mask.dim() == 1:
            head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
            head_mask = head_mask.expand(num_hidden_layers, -1, -1, -1, -1)
        elif head_mask.dim() == 2:
            head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)  # We can specify head_mask for each layer
        assert head_mask.dim() == 5, f"head_mask.dim != 5, instead {head_mask.dim()}"
        head_mask = head_mask.to(dtype=self.dtype)  # switch to float if need + fp16 compatibility
        return head_mask

    def get_extended_attention_mask(
        self,
        attention_mask: paddle.Tensor,
        input_shape: Tuple[int],
        device: (paddle.CPUPlace, paddle.CUDAPlace, str),
        is_decoder: bool,
    ) -> paddle.Tensor:
        """
        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

        Arguments:
            attention_mask (:obj:`paddle.Tensor`):
                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
            input_shape (:obj:`Tuple[int]`):
                The shape of the input to the model.

        Returns:
            :obj:`paddle.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
        """
        if attention_mask.dim() == 3:
            extended_attention_mask = attention_mask[:, None, :, :]
        elif attention_mask.dim() == 2:
            if is_decoder:
                batch_size, seq_length = input_shape
                seq_ids = paddle.arange(end=seq_length)
                causal_mask = (
                    seq_ids[None, None, :].tile(repeat_times=[batch_size, seq_length, 1]) <= seq_ids[None, :, None]
                )
                causal_mask = causal_mask.to(attention_mask.dtype)
                if tuple(causal_mask.shape)[1] < tuple(attention_mask.shape)[1]:
                    prefix_seq_len = tuple(attention_mask.shape)[1] - tuple(causal_mask.shape)[1]
                    causal_mask = paddle.concat(
                        [
                            paddle.ones(
                                shape=(batch_size, seq_length, prefix_seq_len),
                                dtype=causal_mask.dtype,
                            ),
                            causal_mask,
                        ],
                        axis=-1,
                    )
                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
            else:
                extended_attention_mask = attention_mask[:, None, None, :]
        else:
            raise ValueError(
                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
                    input_shape, tuple(attention_mask.shape)
                )
            )
        extended_attention_mask = extended_attention_mask.to(dtype=self._dtype)
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
        return extended_attention_mask

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        is_decoder=False,
        mode="multimodal",
    ):
        """
        encoder_hidden_states  (:obj:`paddle.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (:obj:`paddle.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        past_key_values (:obj:`tuple(tuple(paddle.Tensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
        use_cache (:obj:`bool`, `optional`):
            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
            decoding (see :obj:`past_key_values`).
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if is_decoder:
            use_cache = use_cache if use_cache is not None else self.config.use_cache
        else:
            use_cache = False
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = tuple(input_ids.shape)
            batch_size, seq_length = input_shape
            device = input_ids.place
        elif inputs_embeds is not None:
            input_shape = tuple(inputs_embeds.shape)[:-1]
            batch_size, seq_length = input_shape
            device = inputs_embeds.place
        elif encoder_embeds is not None:
            input_shape = tuple(encoder_embeds.shape)[:-1]
            batch_size, seq_length = input_shape
            device = encoder_embeds.place
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds")
        past_key_values_length = tuple(past_key_values[0][0].shape)[2] if past_key_values is not None else 0
        if attention_mask is None:
            attention_mask = paddle.ones(shape=(batch_size, seq_length + past_key_values_length))
        extended_attention_mask: paddle.Tensor = self.get_extended_attention_mask(
            attention_mask, input_shape, device, is_decoder
        )
        if encoder_hidden_states is not None:
            if type(encoder_hidden_states) == list:
                encoder_batch_size, encoder_sequence_length, _ = tuple(encoder_hidden_states[0].shape)
            else:
                encoder_batch_size, encoder_sequence_length, _ = tuple(encoder_hidden_states.shape)
            encoder_hidden_shape = encoder_batch_size, encoder_sequence_length
            if type(encoder_attention_mask) == list:
                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
            elif encoder_attention_mask is None:
                encoder_attention_mask = paddle.ones(shape=encoder_hidden_shape)
                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
            else:
                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        if encoder_embeds is None:
            embedding_output = self.embeddings(
                input_ids=input_ids,
                position_ids=position_ids,
                inputs_embeds=inputs_embeds,
                past_key_values_length=past_key_values_length,
            )
        else:
            embedding_output = encoder_embeds
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            mode=mode,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]
        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            cross_attentions=encoder_outputs.cross_attentions,
        )


class BertLMHeadModel(BertPreTrainedModel):
    _keys_to_ignore_on_load_unexpected = ["pooler"]
    _keys_to_ignore_on_load_missing = ["position_ids", "predictions.decoder.bias"]

    def __init__(self, config):
        super().__init__(config)
        self.bert = BertModel(config, add_pooling_layer=False)
        self.cls = BertOnlyMLMHead(config)
        self.init_weights()

    def get_output_embeddings(self):
        return self.cls.predictions.decoder

    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        labels=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        return_logits=False,
        is_decoder=True,
        reduction="mean",
        mode="multimodal",
    ):
        """
        encoder_hidden_states  (:obj:`paddle.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (:obj:`paddle.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        labels (:obj:`paddle.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
        past_key_values (:obj:`tuple(tuple(paddle.Tensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
        use_cache (:obj:`bool`, `optional`):
            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
            decoding (see :obj:`past_key_values`).
        Returns:

        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if labels is not None:
            use_cache = False
        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            is_decoder=is_decoder,
            mode=mode,
        )
        sequence_output = outputs[0]
        prediction_scores = self.cls(sequence_output)
        if return_logits:
            return prediction_scores[:, :-1, :].contiguous()
        lm_loss = None
        if labels is not None:
            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
            labels = labels[:, 1:].contiguous()
            loss_fct = paddle.nn.CrossEntropyLoss(reduction=reduction, label_smoothing=0.1)
            lm_loss = loss_fct(
                shifted_prediction_scores.reshape([-1, self.config.vocab_size]),
                labels.reshape([-1]),
            )
            if reduction == "none":
                lm_loss = lm_loss.reshape([prediction_scores.shape[0], -1]).sum(axis=1)
        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return (lm_loss,) + output if lm_loss is not None else output
        return CausalLMOutputWithCrossAttentions(
            loss=lm_loss,
            logits=prediction_scores,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )

    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
        input_shape = tuple(input_ids.shape)
        if attention_mask is None:
            attention_mask = paddle.ones(shape=input_shape, dtype=input_ids.dtype)
        if past is not None:
            input_ids = input_ids[:, -1:]
        return {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "past_key_values": past,
            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
            "is_decoder": True,
        }

    def _reorder_cache(self, past, beam_idx):
        reordered_past = ()
        for layer_past in past:
            reordered_past += (tuple(past_state.index_select(axis=0, index=beam_idx) for past_state in layer_past),)
        return reordered_past
